Forecasting Canadian Dollar against the US Dollar via Combined Approaches

AUTHORS

Atifa Anwar,M. Phil Scholar, Institute of Business Management, Pakistan

ABSTRACT

The purpose of this study is to forecast the Canadian-US dollar exchange rate using both independent and combination models. The fourth model is multivariate, as opposed to the first three, which are univariate. The multivariate model is NARDL, whereas the univariate models are ARIMA, Nave, and Exponential Smoothing. The NARDL is a recent contribution to the literature because it was rarely used for projecting exchange rates in previous studies. The data of exchange rate and other macroeconomic variables ranges from M12011 to M122021. To prevent bias, the authors combine the combination and equally weighted techniques. With a MAPE score of 0.130, the NARDL + Naive model combination outperforms three other solo and combined models.

 

KEYWORDS

Forecasting, NARDL, ARIMA, Naïve, Combined models

REFERENCES

[1] Ullah, M. A., Hassan, M., Rasheed, A., Uddin, I., & Latif, A. “Effect of internal factors on profitability of conventional and Islamic banks of Pakistan. Sindh Economics & Business Review International, vol.1, no.1, pp.47-61, (2019)
[2] Rasheed, A., Asadullah, M., & Uddin, I. Uddin, “PKR exchange rate forecasting through univariate and multivariate time series techniques,” NICE Research Journal, vol.13, no.4, pp.49-67, (2020)
[3] Sabri, R., Asadullah, M., Abdulaziz, A., A., Meero, A., & Ayubi, S, “Forecasting Turkish Lira via combined models approach," Cogent Economics and Finance, vol.10, no.1, pp.1-14, (2022)
[4] Asadullah, M., “Determinants of profitability of Islamic banks of Pakistan–A case study on Pakistan’s Islamic Banking Sector,” International Conference on Advances in Business, Management, and Law, vol.1, no.1, pp.61–73, (2017) DOI:10.30585/icabml-cp.v1i1.13(CrossRef)(Google Scholar)
[5] Asadullah, M., Ahmad, N., & Dos-Santos, M. J. P. L. “Forecast foreign exchange rate: The case study of PKR/USD. Mediterranean Journal of Social Sciences, vol.11, no.4, pp.129-137, (2020), DOI: 10.36941/mjss-2020-0048(CrossRef)(Google Scholar)
[6] Asadullah, M., Bashir, A., & Aleemi, A. R. “Forecasting exchange rates: An empirical application to Pakistani rupee,” Journal of Asian Finance, Economics, and Business, vol.8, no.4, pp.339–347, (2020) DOI:10.1141/2021.jafeb.vol8.n4.339(CrossRef)(Google Scholar)
[7] Uddin, I., Siddiq, Q. U. A., Tabash, M. I., Qayyum, A., & Asadullah, M. “Forecasting Malaysian ringgit against US dollar; individual models vs combined models,” Academy of Accounting and Financial Studies Journal, vol.25, no.6, pp.1-10, (2021)
[8] Uddin, I., Mujahid, H., Tabash, M. I., Ayubi, S., & Ullah, M. A. “Inflation volatility quality of institutions and openness,” Academy of Accounting and Financial Studies Journal, vol.25, no.7, pp. 1-12. (2021a), https://www.abacademies.org/articles/inflationvolatility-quality-of-institutions-and-openness-12073.html
[9] Asadullah, M., Uddin, I., Qayyum, A., Ayubi, S., & Sabri, R. “Forecasting Chinese Yuan,” Journal of Asian Finance, Economics, and Business, vol.8, no.5, pp.221–229, (2020) DOI:10.1231/2021.jafeb.vol8.n5.221(CrossRef)(Google Scholar)
[10] Asadullah, M., Mujahid, H., Tabash, M., I., Ayubi, S., & Sabri, R. “Forecasting Indianrupee/US dollar: ARIMA, exponential smoothing, naive, NARDL, combination techniques,” Academy of Accounting and Financial Studies Journal, vol.25, no.3, pp.1-9. (2021c) DOI:10.31121/aafsj-2021-0025-0001(CrossRef)(Google Scholar)
[11] Asadullah, M., Hassan, M., and Siddiqui, Z. A. “Comparison of Takaful and non-Takaful insurance companies of Pakistan: Under Pre, during, and post-economic Crisis 2008,” Ekonomi, vol.20, no.1, pp.201-212, (2020) DOI: 10.15408/etk.v20i1.17325(CrossRef)(Google Scholar)
[12] Hussain. M and Bashir. U. “Dynamics of trade balance and the j-curve phenomenon: Evidence from Pakistan,” The Journal of Commerce, 5(2), 90-96, (2013)
[13] Moosa I. A. “Univariate time series techniques. In: Exchange rate forecasting: Techniques and applications,” Finance and Capital Markets Series. Palgrave Macmillan, London, (2000)
[14] Khashei, M. and Mahdavi Sharif, B. “A Kalman filter-based hybridization model of statistical and intelligent approaches for exchange rate forecasting", Journal of Modelling in Management, vol.16, no.2, pp.579- 601. (2021) https://doi.org/10.1108/JM2-12-2019-0277(CrossRef)(Google Scholar)
[15] Poon, S. and Granger, C. W. J. “Forecasting volatility in financial markets: A review,” Journal of Economic Literature, vol.41, pp.478-538, (2003)
[16] MacDonald, R., and Marsh, I. W. “Combining exchange rate forecasts: What is the optimal consensus measure?” Journal of Forecasting, 13, 313-333
[17] Matroushi. S. “Hybrid computational intelligence systems based on statistical and neural networks methods for time series forecasting: The case of the gold price,” Master's Thesis, Retrieved from https://researcharchive.lincoln.ac.nz/handle/10182/3986
[18] Dunis, D. L. and Chen, Y. X. “Alternative volatility models for risk management and trading: Application to the EUR/USD and USD/JPY rates,” Derivatives Use, Trading & Regulation, vol.11, no.2, pp.126-156, (2006)
[19] Wang, P. “The economics of foreign exchange and global finance,” 2nd edition. UK: Springer, (2009)
[20] Shahriari, M. “A combined forecasting approaches to exchange rate fluctuations,” International Research Journal of Finance and Economics, vol.79, pp.112-119
[21] Shin, Y., Yu, B., and Greenwood-Nimmo, M. “Modeling asymmetric co-integration and dynamic multipliers in a non-linear ARDL framework,” In: R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Schmidt, pp.281–314. (2014)
[22] Armstrong, J. S. “Principles of forecasting: A handbook for researchers and practitioners,” vol.30, Springer Science & Business Media, (2001)
[23] Deutsch, M., Granger, C.W., and Teräsvirta, T. “The combination of forecasts using changing weights,” International Journal of Forecasting, vol.10, no.1, pp.47-57

CITATION

  • APA:
    Anwar,A.(2023). Forecasting Canadian Dollar against the US Dollar via Combined Approaches. International Journal of Smart Business and Technology, 11(1), 71-78. 10.21742/IJSBT.2023.11.1.05
  • Harvard:
    Anwar,A.(2023). "Forecasting Canadian Dollar against the US Dollar via Combined Approaches". International Journal of Smart Business and Technology, 11(1), pp.71-78. doi:10.21742/IJSBT.2023.11.1.05
  • IEEE:
    [1] A.Anwar, "Forecasting Canadian Dollar against the US Dollar via Combined Approaches". International Journal of Smart Business and Technology, vol.11, no.1, pp.71-78, Mar. 2023
  • MLA:
    Anwar Atifa. "Forecasting Canadian Dollar against the US Dollar via Combined Approaches". International Journal of Smart Business and Technology, vol.11, no.1, Mar. 2023, pp.71-78, doi:10.21742/IJSBT.2023.11.1.05

ISSUE INFO

  • Volume 11, No. 1, 2023
  • ISSN(p):2288-8969
  • ISSN(e):2207-516X
  • Published:Mar. 2023

DOWNLOAD